Literature DB >> 31005183

Which clinical and radiological characteristics can predict clinically significant prostate cancer in PI-RADS 3 lesions? A retrospective study in a high-volume academic center.

Isabeau Hermie1, Jeroen Van Besien2, Pieter De Visschere3, Nicolaas Lumen4, Karel Decaestecker5.   

Abstract

OBJECTIVE: To investigate which clinical and radiological characteristics can predict clinically significant prostate cancer (csPCa) in PI-RADS 3 lesions. To investigate which clinical and radiological characteristics influence the clinician to biopsy a PI-RADS 3 lesion.
MATERIALS AND METHODS: mpMRI PI-RADS 3 lesions scored by 1 out of 3 highly specialized radiologists in a single high-volume center during the period March 2015 to August 2017 were investigated. This score was based on T2 weighted and diffusion weighted imaging (DWI) sequences. Clinical characteristics of all patients with PI-RADS 3 lesions were collected from medical records. Radiological characteristics were collected from radiology reports. Some radiological characteristics such as apparent diffusion coefficient (ADC) in a region of interest at the tumor site and ADC at a site contralateral to the tumor site were calculated on DWI sequences. Cox regression analysis was performed to identify which characteristics could predict csPCa in PI-RADS 3 lesions and which characteristics could influence the behavior of a clinician whether or not to biopsy a PI-RADS 3 lesion.
RESULTS: csPCa could be detected in 31 out of 131 patients with PI-RADS 3 lesions (22.9%). A lower median prostate volume (p = 0.015) and a lower ratio of ADC of the tumor on ADC of the contralateral prostate (ADCT/ADCCLP) (p < 0.001) significantly predisposed for csPCa in multivariate logistic regression. For peripheral zone lesions, a diagnostic model with biopsy of only those PI-RADS 3 lesions with a prostate volume <44 cc and a ratio of ADCT/ADCCLP < 70% showed a sensitivity for detection of csPCa of 59% with a specificity of 88%. (area under the curve 0.780) A suspicious rectal examination (p = 0.011) and the mentioning of prostatitis on the MRI report (p = 0.020) influenced clinicians to biopsy a PI-RADS 3 lesion positively and negatively respectively. For transition zone lesions, previous negative biopsies (p = 0.044) predisposed for csPCa.
CONCLUSION: Prostate volume and the ratio of ADC tumor on ADC of the contralateral prostate have the potential to predict csPCa in PI-RADS 3 lesions with a sensitivity of 59% and specificity of 88%. A suspicious rectal examination and the mentioning of prostatitis on the MRI report influenced the decision of clinicians to biopsy a PI-RADS 3 lesion.
Copyright © 2019 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Clinically significant prostate cancer (csPCa); Multiparametric MRI (mpMRI); Prostate Imaging Reporting and Data System (PI-RADS)

Mesh:

Year:  2019        PMID: 31005183     DOI: 10.1016/j.ejrad.2019.02.031

Source DB:  PubMed          Journal:  Eur J Radiol        ISSN: 0720-048X            Impact factor:   3.528


  13 in total

1.  Clinico-radiological characteristic-based machine learning in reducing unnecessary prostate biopsies of PI-RADS 3 lesions with dual validation.

Authors:  Yansheng Kan; Qing Zhang; Jiange Hao; Wei Wang; Junlong Zhuang; Jie Gao; Haifeng Huang; Jing Liang; Giancarlo Marra; Giorgio Calleris; Marco Oderda; Xiaozhi Zhao; Paolo Gontero; Hongqian Guo
Journal:  Eur Radiol       Date:  2020-06-10       Impact factor: 5.315

2.  A radiomics machine learning-based redefining score robustly identifies clinically significant prostate cancer in equivocal PI-RADS score 3 lesions.

Authors:  Ying Hou; Mei-Ling Bao; Chen-Jiang Wu; Jing Zhang; Yu-Dong Zhang; Hai-Bin Shi
Journal:  Abdom Radiol (NY)       Date:  2020-08-01

3.  Multicenter analysis of clinical and MRI characteristics associated with detecting clinically significant prostate cancer in PI-RADS (v2.0) category 3 lesions.

Authors:  Bashir Al Hussein Al Awamlh; Leonard S Marks; Geoffrey A Sonn; Shyam Natarajan; Richard E Fan; Michael D Gross; Elizabeth Mauer; Samprit Banerjee; Stefanie Hectors; Sigrid Carlsson; Daniel J Margolis; Jim C Hu
Journal:  Urol Oncol       Date:  2020-04-17       Impact factor: 3.498

4.  Evaluating the performance of clinical and radiological data in predicting prostate cancer in prostate imaging reporting and data system version 2.1 category 3 lesions of the peripheral and the transition zones.

Authors:  Caterina Gaudiano; Lorenzo Bianchi; Beniamino Corcioni; Francesca Giunchi; Riccardo Schiavina; Federica Ciccarese; Lorenzo Braccischi; Arianna Rustici; Michelangelo Fiorentino; Eugenio Brunocilla; Rita Golfieri
Journal:  Int Urol Nephrol       Date:  2021-11-25       Impact factor: 2.370

5.  Combining clinical and MRI data to manage PI-RADS 3 lesions and reduce excessive biopsy.

Authors:  Shuo Yang; Wenlu Zhao; Shuangxiu Tan; Yueyue Zhang; Chaogang Wei; Tong Chen; Junkang Shen
Journal:  Transl Androl Urol       Date:  2020-06

6.  Evaluation of a multiparametric MRI radiomic-based approach for stratification of equivocal PI-RADS 3 and upgraded PI-RADS 4 prostatic lesions.

Authors:  Valentina Brancato; Marco Aiello; Luca Basso; Serena Monti; Luigi Palumbo; Giuseppe Di Costanzo; Marco Salvatore; Alfonso Ragozzino; Carlo Cavaliere
Journal:  Sci Rep       Date:  2021-01-12       Impact factor: 4.379

7.  Modified Predictive Model and Nomogram by Incorporating Prebiopsy Biparametric Magnetic Resonance Imaging With Clinical Indicators for Prostate Biopsy Decision Making.

Authors:  Jin-Feng Pan; Rui Su; Jian-Zhou Cao; Zhen-Ya Zhao; Da-Wei Ren; Sha-Zhou Ye; Rui-da Huang; Zhu-Lei Tao; Cheng-Ling Yu; Jun-Hui Jiang; Qi Ma
Journal:  Front Oncol       Date:  2021-09-13       Impact factor: 6.244

8.  Impact of Chronic Prostatitis on the PI-RADS Score 3: Proposal for the Addition of a Novel Binary Suffix.

Authors:  Sascha Merat; Theresa Blümlein; Markus Klarhöfer; Dominik Nickel; Gad Singer; Frank G Zöllner; Stefan O Schoenberg; Rahel A Kubik-Huch; Daniel Hausmann; Lukas Hefermehl
Journal:  Diagnostics (Basel)       Date:  2021-03-30

9.  Considering Predictive Factors in the Diagnosis of Clinically Significant Prostate Cancer in Patients with PI-RADS 3 Lesions.

Authors:  Caleb Natale; Christopher R Koller; Jacob W Greenberg; Joshua Pincus; Louis S Krane
Journal:  Life (Basel)       Date:  2021-12-19

10.  Peripheral zone PSA density: a predominant variable to improve prostate cancer detection efficiency in men with PSA higher than 4 ng ml-1.

Authors:  Cheng Wang; Yue-Yang Wang; Shi-Yuan Wang; Ji-Xiang Ding; Mao Ding; Yuan Ruan; Xiao-Hai Wang; Yi-Feng Jing; Bang-Min Han; Shu-Jie Xia; Chen-Yi Jiang; Fu-Jun Zhao
Journal:  Asian J Androl       Date:  2021 Jul-Aug       Impact factor: 3.285

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